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Huanmei Wu

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Huanmei Wu

  • College of Public Health

    • Health Services Administration and Policy

      • Professor

      • Chair

    • Office of the Dean

      • Assistant Dean for Global Engagement

Biography

Dr. Huanmei Wu, with a BS in chemistry from Tsinghua University and a PhD in computer science from Northeastern University, currently serves as chair of the Department of Health Services Administration and Policy at Temple University's College of Public Health. She also holds the role of assistant dean for global engagement. Before joining Temple, she was chair of the Department of BioHealth Informatics at Indiana University School of Informatics and Computing–Indianapolis.

Dr. Wu is a multi-disciplinary researcher who applies data management and knowledge discovery in the fields of life science and public health. Her research spans a diverse range of topics, including advanced cancer radiotherapy, diabetes, cardiovascular disease, lupus, Alzheimer's disease, and other neurodegenerative conditions, with a strong emphasis on precision treatments and predictive modeling. She collaborates with academia, community health centers, research institutes, industrial partners, and local communities. Dr. Wu's research has secured funding from various agencies, such as NSF, NIH, USAID, PCORI, JDRF, RWJF, and more.

Dr. Wu's research interests include:

  • Managing, analyzing, and modeling integrated multi-source clinical data and social determinants of health for precision healthcare
  • Applying machine learning and artificial intelligence in healthcare
  • Enhancing public health and social well-being through real-world data and evidence
  • Predictive modeling and chronic disease management, encompassing but not limited to diabetes, hypertension, cardiovascular diseases, Alzheimer's disease, ALS, lupus, stroke, and other related conditions

Education

  • PhD, Computer and Information Science, Northeastern University
  • BS, Chemistry, Tsinghua University

Labs: The Lab of Integrative Health Solutions

Courses Taught

Number

Name

Level

HIM 5113

Database Administration for Health Informatics Professionals

Graduate

HIM 8112

Advanced Clinical Decision Support Systems

Graduate

Selected Publications

Recent

  • Ding, H., Ho, K., Searls, E., Low, S., Li, Z., Rahman, S., Madan, S., Igwe, A., Popp, Z., Burk, A., Wu, H., Ding, Y., Hwang, P.H., Anda-Duran, I.D., Kolachalama, V.B., Gifford, K.A., Shih, L.C., Au, R., & Lin, H. (2024). Assessment of Wearable Device Adherence for Monitoring Physical Activity in Older Adults: Pilot Cohort Study. JMIR Aging, 7, e60209. Canada. 10.2196/60209

  • Liu, E., Wu, X., Wang, L., Huo, Y., Wu, H., Li, L., & Cheng, L. (2022). DSCN: Double-target selection guided by CRISPR screening and network. PLoS Comput Biol, 18(8), e1009421. United States. 10.1371/journal.pcbi.1009421

  • Liu, J., Dong, C., Liu, Y., & Wu, H. (2021). CGPE: an integrated online server for Cancer Gene and Pathway Exploration. Bioinformatics, 37(15), 2201-2202. England. 10.1093/bioinformatics/btaa952

  • Yu, H., Lam, K., Green, M.D., Wu, H., Yang, L., Wang, W., Jin, J., Hu, C., Wang, Y., Jolly, S., & Kong, F. (2021). Significance of radiation esophagitis: Conditional survival assessment in patients with non-small cell lung cancer. Journal of the National Cancer Center, 1(2), 31-38. doi: 10.1016/j.jncc.2021.02.003.

  • Zhang, X., Li, C., Wang, X., & Wu, H. (2021). A novel fault diagnosis procedure based on improved symplectic geometry mode decomposition and optimized SVM. Measurement: Journal of the International Measurement Confederation, 173. doi: 10.1016/j.measurement.2020.108644.

  • Patel, J., Lai, P., Dormer, D., Gullapelli, R., Wu, H., & Jones, J.J. (2021). Comparison of Ease of Use and Comfort in Fitness Trackers for Participants Impaired by Parkinson's Disease: An exploratory study. AMIA Jt Summits Transl Sci Proc, 2021, 505-514. United States. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/34457166.

  • Bone, R.N., Oyebamiji, O., Talware, S., Selvaraj, S., Krishnan, P., Syed, F., Wu, H., & Evans-Molina, C. (2020). A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes. Diabetes, 69(11), 2364-2376. United States. 10.2337/db20-0636

  • Liu, J., Dong, C., Jiang, G., Lu, X., Liu, Y., & Wu, H. (2020). Transcription factor expression as a predictor of colon cancer prognosis: a machine learning practice. BMC Med Genomics, 13(Suppl 9), 135. England. 10.1186/s12920-020-00775-0

  • Dong, C., Liu, J., Chen, S.X., Dong, T., Jiang, G., Wang, Y., Wu, H., Reiter, J.L., & Liu, Y. (2020). Highly robust model of transcription regulator activity predicts breast cancer overall survival. BMC Med Genomics, 13(Suppl 5), 49. England. 10.1186/s12920-020-0688-z

  • Li, L., Yu, P., Zhang, P., Wu, H., Chen, Q., Li, S., & Wang, Y. (2020). Upregulation of hsa_circ_0007874 suppresses the progression of ovarian cancer by regulating the miR-760/SOCS3 pathway. Cancer Med, 9(7), 2491-2499. United States. 10.1002/cam4.2866

  • Yu, H., Lam, K., Wu, H., Green, M., Wang, W., Jin, J., Hu, C., Jolly, S., Wang, Y., & Kong, F.S. (2020). Weighted-Support Vector Machine Learning Classifier of Circulating Cytokine Biomarkers to Predict Radiation-Induced Lung Fibrosis in Non-Small-Cell Lung Cancer Patients. Front Oncol, 10, 601979. Switzerland. 10.3389/fonc.2020.601979

  • Jiang, H., Ramadan, A., Laurine, B., Szu-Wei, T., Liu, H., Rowan, C., Liu, X., Wu, H., Wan, J., & Paczesny, S. (2019). IL-33 Therapy Prevents Acute Lung Injury after Transplantation Via IL-9-Producing Type 2 Innate Lymphoid Cells Induction. BLOOD, 134. 10.1182/blood-2019-123821

  • Zhu, W., Liu, X., Xu, M., & Wu, H. (2019). Predicting the results of RNA molecular specific hybridization using machine learning. IEEE/CAA Journal of Automatica Sinica, 6(6), 1384-1396. doi: 10.1109/JAS.2019.1911756.

  • Yu, H., Wu, H., Wang, W., Jolly, S., Jin, J., Hu, C., & Kong, F.S. (2019). Machine Learning to Build and Validate a Model for Radiation Pneumonitis Prediction in Patients with Non-Small Cell Lung Cancer. Clin Cancer Res, 25(14), 4343-4350. United States. 10.1158/1078-0432.CCR-18-1084

  • Kunjan, K., Wu, H., Toscos, T.R., & Doebbeling, B.N. (2019). Large-Scale Data Mining to Optimize Patient-Centered Scheduling at Health Centers. J Healthc Inform Res, 3(1), 1-18. Switzerland. 10.1007/s41666-018-0030-0

  • Hosseini, M., Faiola, A., Jones, J., Vreeman, D.J., Wu, H., & Dixon, B.E. (2019). Impact of document consolidation on healthcare providers' perceived workload and information reconciliation tasks: a mixed methods study. J Am Med Inform Assoc, 26(2), 134-142. England. 10.1093/jamia/ocy158

  • Liu, X., Yue, Z., Cao, Y., Taylor, L., Zhang, Q., Choi, S.W., Hanash, S., Ito, S., Chen, J.Y., Wu, H., & Paczesny, S. (2019). Graft-Versus-Host Disease-Free Antitumoral Signature After Allogeneic Donor Lymphocyte Injection Identified by Proteomics and Systems Biology. JCO Precis Oncol, 3. United States. 10.1200/po.18.00365

  • Lai, P.T., Wilson, J., Wu, H., Jones, J., & Dixon, B.E. (2019). Measuring and Visualizing Chlamydia and Gonorrhea Inequality: An Informatics Approach Using Geographical Information Systems. Online J Public Health Inform, 11(2), e8. Canada. 10.5210/ojphi.v11i2.10155

  • Kechavarzi, B.D., Wu, H., & Doman, T.N. (2019). Bottom-up, integrated -omics analysis identifies broadly dosage-sensitive genes in breast cancer samples from TCGA. PLoS One, 14(1), e0210910. United States. 10.1371/journal.pone.0210910

  • Akino, Y., Wu, H., Oh, R., & Das, I.J. (2019). An effective method to reduce the interplay effects between respiratory motion and a uniform scanning proton beam irradiation for liver tumors: A case study. J Appl Clin Med Phys, 20(1), 220-228. United States. 10.1002/acm2.12508

  • Zhu, W., Wu, H., & Deng, M. (2019). LTL model checking based on binary classification of machine learning. IEEE Access, 7, 135703-135719. doi: 10.1109/ACCESS.2019.2942762.

  • Deng, M., Cao, H., Zhu, W., Wu, H., & Zhou, Y. (2019). Benchmark tests for the model-checking-based ids algorithms. IEEE Access, 7, 135479-135498. doi: 10.1109/ACCESS.2019.2939011.